🐛 의도적 버그 추가: bugbot 테스트용 - 문법오류, 보안취약점, 논리오류, 메모리누수, 타입오류, 마크다운오류 등 포함

[ysys143]

No description provided.

[gitguardian]

⚠️ GitGuardian has uncovered 5 secrets following the scan of your pull request.

Please consider investigating the findings and remediating the incidents. Failure to do so may lead to compromising the associated services or software components.

🔎 Detected hardcoded secrets in your pull request

GitGuardian id	GitGuardian status	Secret	Commit	Filename
17663488	Triggered	Username Password	d00aaa4	.env	View secret
17663376	Triggered	Username Password	8ca44b8	learn/critical_bugs.py	View secret
17663489	Triggered	PostgreSQL Credentials	d00aaa4	.env	View secret
17663490	Triggered	Generic Password	d00aaa4	.env	View secret
17663491	Triggered	Stripe Keys	d00aaa4	.env	View secret

🛠 Guidelines to remediate hardcoded secrets

1. Understand the implications of revoking this secret by investigating where it is used in your code.
2. Replace and store your secrets safely. Learn here the best practices.
3. Revoke and rotate these secrets.
4. If possible, rewrite git history. Rewriting git history is not a trivial act. You might completely break other contributing developers' workflow and you risk accidentally deleting legitimate data.

To avoid such incidents in the future consider

• following these best practices for managing and storing secrets including API keys and other credentials
• install secret detection on pre-commit to catch secret before it leaves your machine and ease remediation.

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^{🦉 GitGuardian detects secrets in your source code to help developers and security teams secure the modern development process. You are seeing this because you or someone else with access to this repository has authorized GitGuardian to scan your pull request.}

[cursor]

Bug: Data-Target Mismatch

Dimension mismatch between data (100 samples) and targets (50 samples) tensors, which will cause runtime errors during training or loss calculation.

learn/buggy_pytorch_example.py#L24-L30

torch_learn/learn/buggy_pytorch_example.py

Lines 24 to 30 in d00aaa4

	# 버그 3: 논리적 오류 - 잘못된 차원 계산
	def create_data():
	# 입력 데이터: (batch_size, features)
	data = torch.randn(100, 10)
	# 버그: 타겟 차원이 입력과 맞지 않음
	targets = torch.randn(50, 5) # 100이어야 하는데 50
	return data, targets

Fix in Cursor

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